Overview

Dataset statistics

Number of variables9
Number of observations3341
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory235.0 KiB
Average record size in memory72.0 B

Variable types

Categorical1
Numeric8

Alerts

Diameter is highly overall correlated with Height and 6 other fieldsHigh correlation
Height is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Length is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Rings is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shell_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shucked_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Viscera_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation

Reproduction

Analysis started2025-12-04 06:29:34.468457
Analysis finished2025-12-04 06:29:38.064878
Duration3.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Sex
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
M
1215 
I
1068 
F
1058 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3341
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowF
4th rowF
5th rowI

Common Values

ValueCountFrequency (%)
M1215
36.4%
I1068
32.0%
F1058
31.7%

Length

2025-12-03T22:29:38.111964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-03T22:29:38.151169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m1215
36.4%
i1068
32.0%
f1058
31.7%

Most occurring characters

ValueCountFrequency (%)
M1215
36.4%
I1068
32.0%
F1058
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M1215
36.4%
I1068
32.0%
F1058
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M1215
36.4%
I1068
32.0%
F1058
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M1215
36.4%
I1068
32.0%
F1058
31.7%

Length
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52263394
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.198272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.29
Q10.45
median0.545
Q30.615
95-th percentile0.69
Maximum0.815
Range0.74
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.12053122
Coefficient of variation (CV)0.23062265
Kurtosis0.078392114
Mean0.52263394
Median Absolute Deviation (MAD)0.08
Skewness-0.66177892
Sum1746.12
Variance0.014527776
MonotonicityNot monotonic
2025-12-03T22:29:38.256825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57582
 
2.5%
0.62574
 
2.2%
0.5574
 
2.2%
0.6271
 
2.1%
0.5870
 
2.1%
0.568
 
2.0%
0.666
 
2.0%
0.5765
 
1.9%
0.52563
 
1.9%
0.6162
 
1.9%
Other values (122)2646
79.2%
ValueCountFrequency (%)
0.0751
 
< 0.1%
0.111
 
< 0.1%
0.132
 
0.1%
0.142
 
0.1%
0.151
 
< 0.1%
0.1552
 
0.1%
0.164
0.1%
0.1655
0.1%
0.172
 
0.1%
0.1752
 
0.1%
ValueCountFrequency (%)
0.8151
 
< 0.1%
0.81
 
< 0.1%
0.782
 
0.1%
0.7752
 
0.1%
0.771
 
< 0.1%
0.7651
 
< 0.1%
0.7553
 
0.1%
0.758
0.2%
0.7454
0.1%
0.746
0.2%

Diameter
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4066088
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.313349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.215
Q10.35
median0.425
Q30.48
95-th percentile0.545
Maximum0.65
Range0.595
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.099482084
Coefficient of variation (CV)0.24466289
Kurtosis-0.024896081
Mean0.4066088
Median Absolute Deviation (MAD)0.065
Skewness-0.63309538
Sum1358.48
Variance0.0098966849
MonotonicityNot monotonic
2025-12-03T22:29:38.366991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45116
 
3.5%
0.475100
 
3.0%
0.493
 
2.8%
0.592
 
2.8%
0.4780
 
2.4%
0.4879
 
2.4%
0.4474
 
2.2%
0.4672
 
2.2%
0.4271
 
2.1%
0.52569
 
2.1%
Other values (101)2495
74.7%
ValueCountFrequency (%)
0.0551
 
< 0.1%
0.091
 
< 0.1%
0.0951
 
< 0.1%
0.12
 
0.1%
0.1054
0.1%
0.113
0.1%
0.1152
 
0.1%
0.125
0.1%
0.1254
0.1%
0.137
0.2%
ValueCountFrequency (%)
0.651
 
< 0.1%
0.633
0.1%
0.6251
 
< 0.1%
0.621
 
< 0.1%
0.6151
 
< 0.1%
0.611
 
< 0.1%
0.6051
 
< 0.1%
0.64
0.1%
0.5953
0.1%
0.594
0.1%

Height
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13888656
Minimum0
Maximum0.515
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.424302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q10.115
median0.14
Q30.165
95-th percentile0.2
Maximum0.515
Range0.515
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.039272989
Coefficient of variation (CV)0.28277026
Kurtosis2.2771641
Mean0.13888656
Median Absolute Deviation (MAD)0.025
Skewness-0.011299628
Sum464.02
Variance0.0015423677
MonotonicityNot monotonic
2025-12-03T22:29:38.478334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15219
 
6.6%
0.14177
 
5.3%
0.155172
 
5.1%
0.16172
 
5.1%
0.175167
 
5.0%
0.125164
 
4.9%
0.165154
 
4.6%
0.135152
 
4.5%
0.145138
 
4.1%
0.12137
 
4.1%
Other values (40)1689
50.6%
ValueCountFrequency (%)
02
 
0.1%
0.011
 
< 0.1%
0.0152
 
0.1%
0.022
 
0.1%
0.0255
 
0.1%
0.036
 
0.2%
0.0356
 
0.2%
0.046
 
0.2%
0.04510
0.3%
0.0515
0.4%
ValueCountFrequency (%)
0.5151
 
< 0.1%
0.253
 
0.1%
0.243
 
0.1%
0.2356
 
0.2%
0.239
 
0.3%
0.22511
 
0.3%
0.2210
 
0.3%
0.21525
0.7%
0.2118
0.5%
0.20536
1.1%

Whole_weight
Real number (ℝ)

High correlation 

Distinct2139
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82162332
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.525663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.121
Q10.4405
median0.7955
Q31.1445
95-th percentile1.6705
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.704

Descriptive statistics

Standard deviation0.48580909
Coefficient of variation (CV)0.59127958
Kurtosis0.027731137
Mean0.82162332
Median Absolute Deviation (MAD)0.3525
Skewness0.52940873
Sum2745.0435
Variance0.23601047
MonotonicityNot monotonic
2025-12-03T22:29:38.572375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22256
 
0.2%
0.47756
 
0.2%
0.4875
 
0.1%
1.0655
 
0.1%
1.08355
 
0.1%
1.13455
 
0.1%
0.58055
 
0.1%
0.87255
 
0.1%
0.63355
 
0.1%
0.4945
 
0.1%
Other values (2129)3289
98.4%
ValueCountFrequency (%)
0.0021
< 0.1%
0.0081
< 0.1%
0.01051
< 0.1%
0.0131
< 0.1%
0.0141
< 0.1%
0.01452
0.1%
0.0151
< 0.1%
0.01751
< 0.1%
0.0182
0.1%
0.0191
< 0.1%
ValueCountFrequency (%)
2.82551
< 0.1%
2.77951
< 0.1%
2.6571
< 0.1%
2.5551
< 0.1%
2.5481
< 0.1%
2.5261
< 0.1%
2.51551
< 0.1%
2.50851
< 0.1%
2.5051
< 0.1%
2.4991
< 0.1%

Shucked_weight
Real number (ℝ)

High correlation 

Distinct1405
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35598069
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.791600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0505
Q10.1845
median0.3345
Q30.496
95-th percentile0.7345
Maximum1.488
Range1.487
Interquartile range (IQR)0.3115

Descriptive statistics

Standard deviation0.21976934
Coefficient of variation (CV)0.6173631
Kurtosis0.60628962
Mean0.35598069
Median Absolute Deviation (MAD)0.157
Skewness0.71159308
Sum1189.3315
Variance0.048298565
MonotonicityNot monotonic
2025-12-03T22:29:38.845115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0979
 
0.3%
0.28
 
0.2%
0.15758
 
0.2%
0.20158
 
0.2%
0.3028
 
0.2%
0.1658
 
0.2%
0.2618
 
0.2%
0.21658
 
0.2%
0.1437
 
0.2%
0.0967
 
0.2%
Other values (1395)3262
97.6%
ValueCountFrequency (%)
0.0011
 
< 0.1%
0.00251
 
< 0.1%
0.00452
0.1%
0.0053
0.1%
0.00552
0.1%
0.00652
0.1%
0.0071
 
< 0.1%
0.00754
0.1%
0.00851
 
< 0.1%
0.0092
0.1%
ValueCountFrequency (%)
1.4881
< 0.1%
1.3511
< 0.1%
1.34851
< 0.1%
1.2531
< 0.1%
1.2321
< 0.1%
1.19651
< 0.1%
1.19451
< 0.1%
1.15651
< 0.1%
1.14951
< 0.1%
1.14651
< 0.1%

Viscera_weight
Real number (ℝ)

High correlation 

Distinct838
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17905507
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:38.908123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0265
Q10.094
median0.1685
Q30.2495
95-th percentile0.3735
Maximum0.76
Range0.7595
Interquartile range (IQR)0.1555

Descriptive statistics

Standard deviation0.10846749
Coefficient of variation (CV)0.60577727
Kurtosis0.21656768
Mean0.17905507
Median Absolute Deviation (MAD)0.0775
Skewness0.61051403
Sum598.223
Variance0.011765197
MonotonicityNot monotonic
2025-12-03T22:29:38.966631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026512
 
0.4%
0.15612
 
0.4%
0.19611
 
0.3%
0.145511
 
0.3%
0.171511
 
0.3%
0.219510
 
0.3%
0.13110
 
0.3%
0.162510
 
0.3%
0.11510
 
0.3%
0.09910
 
0.3%
Other values (828)3234
96.8%
ValueCountFrequency (%)
0.00052
 
0.1%
0.0021
 
< 0.1%
0.00252
 
0.1%
0.0032
 
0.1%
0.00352
 
0.1%
0.0041
 
< 0.1%
0.00454
0.1%
0.0057
0.2%
0.00554
0.1%
0.0062
 
0.1%
ValueCountFrequency (%)
0.761
< 0.1%
0.64151
< 0.1%
0.591
< 0.1%
0.5751
< 0.1%
0.57451
< 0.1%
0.5641
< 0.1%
0.5412
0.1%
0.52651
< 0.1%
0.5251
< 0.1%
0.52351
< 0.1%

Shell_weight
Real number (ℝ)

High correlation 

Distinct847
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23743071
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:39.020625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0355
Q10.13
median0.23
Q30.325
95-th percentile0.476
Maximum1.005
Range1.0035
Interquartile range (IQR)0.195

Descriptive statistics

Standard deviation0.13886283
Coefficient of variation (CV)0.58485623
Kurtosis0.68635073
Mean0.23743071
Median Absolute Deviation (MAD)0.099
Skewness0.63879025
Sum793.256
Variance0.019282885
MonotonicityNot monotonic
2025-12-03T22:29:39.078166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27534
 
1.0%
0.33533
 
1.0%
0.18533
 
1.0%
0.2533
 
1.0%
0.31532
 
1.0%
0.331
 
0.9%
0.2430
 
0.9%
0.1730
 
0.9%
0.29529
 
0.9%
0.1929
 
0.9%
Other values (837)3027
90.6%
ValueCountFrequency (%)
0.00151
 
< 0.1%
0.0031
 
< 0.1%
0.00351
 
< 0.1%
0.0042
 
0.1%
0.00511
0.3%
0.0061
 
< 0.1%
0.0071
 
< 0.1%
0.00751
 
< 0.1%
0.0081
 
< 0.1%
0.00851
 
< 0.1%
ValueCountFrequency (%)
1.0051
< 0.1%
0.8971
< 0.1%
0.8852
0.1%
0.851
< 0.1%
0.8151
< 0.1%
0.79751
< 0.1%
0.781
< 0.1%
0.761
< 0.1%
0.7261
< 0.1%
0.7252
0.1%

Rings
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9254714
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2025-12-03T22:29:39.124636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median9
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2455609
Coefficient of variation (CV)0.32699312
Kurtosis2.3102557
Mean9.9254714
Median Absolute Deviation (MAD)2
Skewness1.0985842
Sum33161
Variance10.533665
MonotonicityNot monotonic
2025-12-03T22:29:39.165658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9563
16.9%
10504
15.1%
8440
13.2%
11390
11.7%
7302
9.0%
12216
 
6.5%
6216
 
6.5%
13159
 
4.8%
14102
 
3.1%
593
 
2.8%
Other values (17)356
10.7%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
314
 
0.4%
450
 
1.5%
593
 
2.8%
6216
 
6.5%
7302
9.0%
8440
13.2%
9563
16.9%
10504
15.1%
ValueCountFrequency (%)
291
 
< 0.1%
272
 
0.1%
251
 
< 0.1%
241
 
< 0.1%
237
 
0.2%
226
 
0.2%
2111
 
0.3%
2022
0.7%
1924
0.7%
1836
1.1%

Interactions

2025-12-03T22:29:37.553889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.606092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.018203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.497260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.868929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.256866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.665653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.069453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.610181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.672192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.177402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.545555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.917424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.312554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.727161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.234773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.657093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.717867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.217509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.598908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.960496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.365067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.776961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.277491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.703229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.763280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.266594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.641405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.003501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.416067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.822956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.324463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.749122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.823672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.310594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.683403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.047427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.463583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.870886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.365567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.796638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.873953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.356117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.730417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.103452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.509591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.918835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.412135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.851172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.925950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.407111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.773907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.152044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.557266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.972363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.461112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.902176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:34.973205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.452502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:35.819909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.201043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:36.611438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.021891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-03T22:29:37.505129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-03T22:29:39.209294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DiameterHeightLengthRingsSexShell_weightShucked_weightViscera_weightWhole_weight
Diameter1.0000.8950.9830.6260.4050.9540.9490.9480.971
Height0.8951.0000.8860.6630.3940.9220.8730.8980.915
Length0.9830.8861.0000.6050.3950.9470.9560.9520.972
Rings0.6260.6630.6051.0000.3610.6960.5430.6170.634
Sex0.4050.3940.3950.3611.0000.4160.3950.4260.425
Shell_weight0.9540.9220.9470.6960.4161.0000.9160.9370.969
Shucked_weight0.9490.8730.9560.5430.3950.9161.0000.9480.977
Viscera_weight0.9480.8980.9520.6170.4260.9370.9481.0000.975
Whole_weight0.9710.9150.9720.6340.4250.9690.9770.9751.000

Missing values

2025-12-03T22:29:37.968009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-03T22:29:38.017344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weightRings
0I0.4300.3250.1100.36750.13550.09350.120013
1I0.3150.2300.0000.13400.05750.02850.35056
2F0.5450.4350.1500.68550.29050.14500.225010
3F0.6550.5100.1551.28950.53450.28550.410011
4I0.2700.2000.0700.10000.03400.02450.03505
5M0.4650.3600.0800.48800.19100.12500.155011
6F0.5650.4800.1750.95700.38850.21500.275018
7M0.5700.4800.1751.18500.47400.26100.380011
8I0.6200.4800.1801.13050.52850.26550.306012
9I0.5400.4200.1400.72650.32050.14450.22909
SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weightRings
3331M0.4500.3550.1150.47800.18000.11850.155010
3332M0.5950.4650.1250.79900.32450.20000.230010
3333F0.3800.3200.1150.64750.32300.13250.16407
3334M0.5200.4100.1150.77000.26300.15700.260011
3335I0.3100.2300.0700.12450.05050.02650.03806
3336F0.5150.3950.1400.68600.28100.12550.220012
3337F0.5650.4500.1350.98850.38700.14950.310012
3338F0.5800.4400.1751.07300.40050.23450.335019
3339M0.5750.4500.1300.78500.31800.19300.22659
3340M0.6650.5150.1651.38550.62100.30200.34458